Data Analysis in Python (PYDATA1)

Databases, Data Analytics

Do you want to gain deeper insights from your data? Are you no longer satisfied with mere data processing using Excel and SQL and crave full control over their processing? Would you like to automate the entire data processing process so you can delve into more interesting data analyses?

Sign up for a practical workshop where you will learn to process data using the powerful Pandas library and visualize it using the Matplotlib library. Record the entire data processing procedure in the practical tool Jupyter Notebook, where you carefully document each step and process, plot, and present data in graphs.

COURSE LOCATION AND AVAILABLE DATES



Choose whether to attend in person in our classroom or join online. You can select your preferred format during registration. Learn more about hybrid training.

Public courses are usually delivered in Czech, but this course is also available in English. We can arrange private training for your team online, at your premises or in our classrooms, and tailor the content to your needs.

For groups of around 4 or more participants, private training can already be comparable in price to booking individual places on a public course. Send us your requirements and we’ll recommend the best format and provide an exact quote.

Request training in English

Course content:

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  • Data analysis tools
    1. Problem formulation
    2. Statistical methods
  • Jupyter notebook
    1. Installation and launch
    2. Text and code in one document
    3. Running individual steps
    4. Differences from regular work in Python
  • Data tools
    1. Pandas and related libraries
    2. DataFrame and Series
    3. Processing tabular data
  • Data sources
    1. Tabular formats (Excel, CSV)
    2. Database sources (SQL)
    3. Web sources (webscraping)
  • Data processing
    1. Transformation of tabular data
    2. Filtering rows and columns
    3. Conversion of data types
    4. Data aggregation
  • Statistical tools
    1. Statistical properties of data
    2. Verification of correlations
  • Outputs and presentation of results
    1. Tables and graphs
    2. Data formats and SQL
  • Where to continue
    1. Other libraries and tools
    2. Machine learning options
Prerequisites:
Basic knowledge of the Python language.
Recommended previous course:
Python – Programming Basics (PYTH1)
Recommended follow-up course:
Advanced Data Analysis in Python (PYDATA2)
Schedule:
2 days (9:00-17:00)
Price per person:
392.00 € ( 474.32 € incl. 21% VAT)

Training and learning environment